<![CDATA[This study aims to analyze the number of Indomaret outlets in Jakarta by utilizing informationsystems technology and data mining techniques. Using quantitative data from 500 Indomaretlocations, the analysis was conducted to identify distribution patterns and the factors influencingoutlet growth. Clustering and linear regression methods were employed to evaluate the relationshipbetween the number of outlets and demographic and economic variables, such as population density,per capita income, and distance from the city center. The analysis results indicate a significantrelationship between population density and the number of Indomaret outlets, with a regressioncoefficient of 0.75 (p < 0.01), meaning that every increase of 1,000 people in population density isassociated with the addition of 3 Indomaret outlets. Clustering analysis also identified three strategiclocation groups with high growth potential. The main contribution of this research lies in integratingdata mining methods with spatial analysis to understand modern retail expansion in urban areas—anapproach that is still rarely explored in previous studies. These findings not only enrich the literatureon data-driven retail location analysis but also provide practical insights for industry players informulating data-based expansion strategies. This research offers valuable insights for Indomaret’smanagement in making strategic decisions regarding expansion and store placement, demonstratingthat the use of information systems and data mining is effective in supporting quantitative analysisfor business development in the retail sector.]]>
Copyrights © 2025
